Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

Respiratory Motion Correction on 3D Positron Emission Tomography Images

PET/CT Gräte erlauben gleichzeitige morphologische und anatomische Bildaufnahme des Körpers. Die Aufnahmemodalitäten bedingen, dass bei der Positronen-Emissions-Tomographie (PET) der Patient weiter Atmet. Bei der Computer Tomographie (CT) dagegen, die nur wenige Sekunden dauert, hält er seinen Atem. Aufgrund der Diskrepanz zwischen den Aufnahmen kommt es zu Artefakten bei der Gewichtung der PET-Daten durch die CT-Daten. Diese Gewichtung ist aber für Quantitative PET-Daten notwendig. Des Weiteren können kleine Tumore durch die Verschmierung der Daten im Rauschen untergehen. In dieser Arbeit wird eine Lösung des Problems vorgeschlagen die auf zwei Schritte beruht. Zunächst werden die PET-Daten in verschiedene Atemphasen unterteilt. Im zweiten Schritt werden die Daten verschiedener Phasen mit einer Zielphase in Übereinstimmung gebracht. Hierzu wird eine Optical Flow Methode benutzt. Die Ergebnisse auf Phantom und auf Patientendaten zeigen, dass das Problem erfolgreich gelöst worden ist

Tools for improving high-dose-rate prostate cancer brachytherapy using three-dimensional ultrasound and magnetic resonance imaging

High-dose-rate brachytherapy (HDR-BT) is an interstitial technique for the treatment of intermediate and high-risk localized prostate cancer that involves placement of a radiation source directly inside the prostate using needles. Dose-escalated whole-gland treatments have led to improvements in survival, and tumour-targeted treatments may offer future improvements in therapeutic ratio. The efficacy of tumour-targeted HDR-BT depends on imaging tools to enable accurate dose delivery to prostate sub-volumes. This thesis is focused on implementing ultrasound tools to improve HDR-BT needle localization accuracy and efficiency, and evaluating dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for tumour localization. First, we implemented a device enabling sagittally-reconstructed 3D (SR3D) ultrasound, which provides sub-millimeter resolution in the needle insertion direction. We acquired SR3D and routine clinical images in a cohort of 12 consecutive eligible HDR-BT patients, with a total of 194 needles. The SR3D technique provided needle insertion depth errors within 5 mm for 93\% of needles versus 76\% for the clinical imaging technique, leading to increased precision in dose delivered to the prostate. Second, we implemented an algorithm to automatically segment multiple HDR-BT needles in a SR3D image. The algorithm was applied to the SR3D images from the first patient cohort, demonstrating mean execution times of 11.0 s per patient and successfully segmenting 82\% of needles within 3 mm. Third, we augmented SR3D imaging with live-2D sagittal ultrasound for needle tip localization. This combined technique was applied to another cohort of 10 HDR-BT patients, reducing insertion depth errors compared to routine imaging from a range of [-8.1 mm, 7.7 mm] to [-6.2 mm, 5.9 mm]. Finally, we acquired DCE-MRI in 16 patients scheduled to undergo prostatectomy, using either high spatial resolution or high temporal resolution imaging, and compared the images to whole-mount histology. The high spatial resolution images demonstrated improved high-grade cancer classification compared to the high temporal resolution images, with areas under the receiver operating characteristic curve of 0.79 and 0.70, respectively. In conclusion, we have translated and evaluated specialized imaging tools for HDR-BT which are ready to be tested in a clinical trial investigating tumour-targeted treatment

Surrogate-driven motion models from cone-beam CT for motion management in radiotherapy treatments

This thesis details a variety of methods to build a surrogate-driven motion model from a cone-beam CT (CBCT) scan. The methods are intended to form a key constituent of a tracked RT treatment system, by providing a markerless means of tracking tumour and organs at risk (OAR) positions in real-time. The beam can then be adjusted to account for the respiratory motion of the tumour, whilst ensuring no adverse e.ects on the OAR from the adjustment in the beam. An approach to describe an iterative method to markerlessly track the lung tumour region is presented. A motion model is built of the tumour region using the CBCT projections, which then gives tumour position information during treatment. For simulated data, the motion model was able to reduce the mean L2-norm error from 4.1 to 1.0 mm, relative to the mean position. The model was used to account for the motion of an object placed within a respiratory phantom. When used to perform a motion compensated reconstruction (MCR), measured dimensions of this object agreed to within the voxel size (1 mm cube) used for the reconstruction. The method was applied to 6 clinical datasets. Improvements in edge contrast of the tumour were seen, and compared to clinically-derived positions for the tumour centres, the mean absolute errors in superior-inferior directions was reduced to under 2.5 mm. The model is then subsequently extended to monitor both tumour and OAR regions during treatment. This extended approach uses both the planning 4DCT and CBCT scans, focusing on the strengths of each respective dataset. Results are presented on three simulated and three clinical datasets. For the simulated data, maximal L2-norm errors were reduced from 14.8 to 4.86 mm. Improvements in edge contrast in the diaphragm and lung regions were seen in the MCR for the clinical data. A final approach to building a model of the entire patient is then presented, utilising only the CBCT data. An optical-flow-based approach is taken, which is adapted to the unique nature of the CBCT data via some interesting conceptualisations. Results on a simulated case are presented, showing increased edge contrast in the MCR using the fitted motion model. Mean L2-norm errors in the tumour region were reduced from 4.2 to 2.6 mm. Future work is discussed, with a variety of extensions to the methods proposed. With further development, it is hoped that some of the ideas detailed could be translated into the clinic and have a direct impact on patient treatment

Plausibility of Image Reconstruction Using a Proposed Flexible and Portable CT Scanner

The very hot and power-hungry x-ray filaments in today's computed tomography (CT) scanners constrain their design to be big and stationary. What if we built a CT scanner that could be deployed at the scene of a car accident to acquire tomographic images before moving the victim? Recent developments in nanotechnology have shown that carbon nanotubes can produce x-rays at room temperature, and with relatively low power needs. We propose a design for a portable and flexible CT scanner made up of an addressable array of tiny x-ray emitters and detectors. In this paper, we outline a basic design, propose a strategy for reconstruction, and demonstrate the feasibility of reconstruction using experiments on a software simulation of the flexible scanner. These simulations show that reconstruction quality is stable over a wide range of scanner geometries, while progressively larger errors in the scanner geometry induce progressively larger errors. We also raise a number of issues that still need to be overcome to build such a scanner.This work was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation, and the Ontario Innovation Trust

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Segmenting the male pelvic organs from limited angle images with application to ART

Prostate cancer is the second leading cause of cancer deaths in men, and external beam radiotherapy is a common method for treating prostate cancer. In a clinically state-of-the-art radiotherapy protocol, CT images are taken at treatment time and are used to properly position the patient with respect to the treatment device. In adaptive radiotherapy (ART), this image is used to approximate the actual radiation dose delivered to the patient and track the progress of therapy. Doing so, however, requires that the male pelvic organs of interest be segmented and that correspondence be established between the images (registration), such that cumulative delivered dose can be accumulated in a reference coordinate system. Because a typical prostate radiotherapy treatment is delivered over 30-40 daily fractions, there is a large non-therapeutic radiation dose delivered to the patient from daily imaging. In the interest of reducing this dose, gantry mounted limited angle imaging devices have been developed which reduce dose at the expense of image quality. However, in the male pelvis, such limited angle images are not suitable for the ART process using traditional methods. In this work, a patient specific deformation model is developed that is sufficient for use with limited angle images. This model is learned from daily CT images taken during the first several treatment fractions. Limited angle imaging can then be used for the remaining fractions at decreased dose. When the parameters of this model are set, it provides segmentation of the prostate, bladder, and rectum, correspondence between the images, and a CT-like image that can be used for dose accumulation. However, intra-patient deformation in the male pelvis is complex and quality deformation models cannot be developed from a reasonable number of training images using traditional methods. This work solves this issue by partitioning the deformation to be explained into independent sub-models that explain deformation due to articulation, deformation near to the skin, deformation of the prostate bladder, and rectum, and any residual deformation. It is demonstrated that a model that segments the prostate with accuracy comparable to inter-expert variation can be developed from 16 daily images.Doctor of Philosoph